Quantitative cardiovascular MR (CMR) imaging has the potential to perform a wide range of diagnostic measurements in the heart, providing reproducible, accurate assessments of heart function and anatomy for diagnosis and monitoring of cardiovascular diseases in humans and in animal models. For example, quantification of the NMR relaxation constants T1 and T2 is promising for cardiovascular tissue characterization, revealing fibrosis, edema, inflammation, and more. Further, quantification of myocardial blood flow (MBF) through myocardial perfusion imaging is promising for diagnosing ischemia and coronary artery disease. However, imaging in the presence of various overlapping dynamics—both physiological (e.g., cardiac and respiratory motion) and physical (e.g., T1 and T2 relaxation)—is a major technical challenge which has prevented widespread adoption of quantitative CMR.
The conventional strategy to handle the overlapping dynamics involved in cardiovascular imaging has been to apply a complicated mixture of ECG control, breath holding, and/or short acquisition bursts to “freeze” as many dynamics as possible during data acquisition. This typically means choosing one dynamic at a time, forgoing useful information about the remaining dynamics and requiring pauses in between acquisition bursts. As a result, the standard cardiac exam consists of a prolonged, inefficient sequence of scans, each of which applies a different combination of freezing mechanisms targeting different dynamics. Furthermore, these freezing mechanisms can be unreliable (e.g., ECG triggering) or uncomfortable (e.g., breath holds), and the use of multiple breath holds results in misalignment between scans, complicating image fusion for comprehensive analysis. More importantly, this overall strategy does not work properly for particularly unhealthy subjects who have cardiac arrhythmias or difficulty holding their breath. Accordingly, there is a need for addressing the overlapping dynamics in cardiovascular imaging so as to make quantitative CMR feasible.